import os
import pandas
import numpy as np
from matplotlib import pyplot as plt
from matplotlib import ticker
import matplotlib.patches as mpatches
from IPython.display import display, HTML
import geopandas as gpd
import time
import io
from pprint import pprint
os.chdir('/home/idies/workspace/Temporary/raddick/cra_scratch/')
print(os.getcwd())
outdir = '/home/idies/workspace/Storage/raddick/Baltimore/community_reinvestment_act/'
print(outdir)
shapefiledir = '/home/idies/workspace/Storage/raddick/Baltimore/shapefiles/'
print(shapefiledir)
print('DONE')

/home/idies/miniconda3/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
  return f(*args, **kwds)
/home/idies/miniconda3/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
  return f(*args, **kwds)

/home/idies/workspace/Temporary/raddick/cra_scratch
/home/idies/workspace/Storage/raddick/Baltimore/community_reinvestment_act/
/home/idies/workspace/Storage/raddick/Baltimore/shapefiles/
DONE

Load shapefiles¶

s = time.time()
print('reading shapefile...')

tract_shapes_gdf = gpd.read_file(shapefiledir+'census_tracts_2010/geo_export_c50bbe56-543e-4878-9c9f-c56be327600a.shp', encoding='utf-8')
tract_shapes_gdf = tract_shapes_gdf.assign(tractname = pandas.to_numeric(tract_shapes_gdf['name'].apply(lambda x: x.split(' ')[-1]), errors='coerce'))
tract_shapes_gdf = tract_shapes_gdf.set_index('tractname')
e = time.time()
print('Read {0:,.0f} Maryland census tracts in {1:.3f} seconds.'.format(len(tract_shapes_gdf), e-s))

print('Calculating percent white...')
tract_shapes_gdf = tract_shapes_gdf.assign(percent_white = tract_shapes_gdf['white'] / tract_shapes_gdf['population'])

print('\nreading city boundary...')
boundary_gdf = gpd.read_file(shapefiledir+'baltimore_city_polygon/baltimore_city_polygon.shp')

print('\nreading water features...')
water_gdf = gpd.read_file(shapefiledir+'water/water.shp', encoding='utf-8')
water_gdf = water_gdf.set_index('OBJECTID')

s = time.time()
print('\nreading street centerlines...')
streets_gdf = gpd.read_file(shapefiledir+'streets/streetcl.shp', encoding='utf-8')
e = time.time()
print('Read {0:,.0f} streets in {1:.3f} seconds.'.format(len(streets_gdf), e-s))

s = time.time()
streets_gdf = streets_gdf.drop('OBJECTID_1', axis=1)
streets_gdf = streets_gdf.set_index('OBJECTID')
print('Cutting off streets at city boundary...')
s = time.time()
streets_gdf = gpd.sjoin(streets_gdf, boundary_gdf, op='within')
e = time.time()
print('Remaining: {0:,.0f} streets after {1:,.0f} seconds of processing.'.format(len(streets_gdf), e-s))
#streets_gdf = gpd.overlay(streets_gdf, boundary_gdf, how='intersection')

#print('\nSetting CRSes...')
#for x in (boundary_gdf, water_gdf, streets_gdf):
#    x.crs = tract_shapes_gdf.crs

print('DONE')

reading shapefile...
Read 200 Maryland census tracts in 0.106 seconds.
Calculating percent white...

reading city boundary...

reading water features...

reading street centerlines...
Read 48,160 streets in 9.734 seconds.
Cutting off streets at city boundary...

/home/idies/miniconda3/lib/python3.6/site-packages/geopandas/tools/sjoin.py:44: UserWarning: CRS of frames being joined does not match!
  warn('CRS of frames being joined does not match!')

Remaining: 47,642 streets after 14 seconds of processing.
DONE

Lending by geography of borrowers¶

Load all data¶

s = time.time()
baltimore_tracts_df = pandas.read_csv('tracts_processed.csv', encoding='utf-8', index_col='rownumber')
print('{0:,.0f} tract datas nationwide.'.format(len(baltimore_tracts_df)))
print('backing up...')
baltimore_tracts_df_bk = baltimore_tracts_df
e = time.time()
print('DONE in {0:,.0f} seconds.'.format(e-s))

/home/idies/miniconda3/lib/python3.6/site-packages/numpy/lib/arraysetops.py:518: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison
  mask |= (ar1 == a)

22,761,175 tract datas nationwide.
backing up...
DONE in 97 seconds.

Select Baltimore City, convert income tract data to low/moderate/medium/high¶

print('retrieving from backup...')
baltimore_tracts_df = baltimore_tracts_df_bk

baltimore_tracts_df = baltimore_tracts_df[(baltimore_tracts_df['state'] == 24) & (baltimore_tracts_df['county'] == 510)]
print('{0:,.0f} tract datas in Baltimore.'.format(len(baltimore_tracts_df)))

baltimore_tracts_df = baltimore_tracts_df[baltimore_tracts_df['loan_indicator'] == 'Y']
print('{0:,.0f} of them had loans.'.format(len(baltimore_tracts_df)))

print('Converting full level system to low/moderate/medium/upper...')
baltimore_tracts_df = baltimore_tracts_df.assign(cra_level = '')
baltimore_tracts_df.loc[(baltimore_tracts_df['income_group_total'].apply(lambda x: (x >= 1) & (x <=5))), 'cra_level'] = 'low'
baltimore_tracts_df.loc[(baltimore_tracts_df['income_group_total'].apply(lambda x: (x >= 6) & (x <=8))), 'cra_level'] = 'moderate'
baltimore_tracts_df.loc[(baltimore_tracts_df['income_group_total'].apply(lambda x: (x >= 9) & (x <=12))), 'cra_level'] = 'middle'
baltimore_tracts_df.loc[(baltimore_tracts_df['income_group_total'].apply(lambda x: (x == 13))), 'cra_level'] = 'upper'
baltimore_tracts_df.loc[(baltimore_tracts_df['income_group_total'].apply(lambda x: (x >= 14) & (x <=15))), 'cra_level'] = 'unknown'

print('backing up...')
baltimore_tracts_df_bk = baltimore_tracts_df
print('DONE')

retrieving from backup...
61,098 tract datas in Baltimore.
26,929 of them had loans.
Converting full level system to low/moderate/medium/upper...
backing up...
DONE

Aggregate all loans within census tracts¶

print('retrieving from backup...')
baltimore_tracts_df = baltimore_tracts_df_bk

print('grouping...')

tractdata = baltimore_tracts_df[baltimore_tracts_df['loan_indicator'] == 'Y'].groupby(['census_tract', 'activity_year', 'institution_name']).size()

tractdata = tractdata.sort_index()
# Run this to get every index value to display in every row:
#pandas.set_option('display.multi_sparse', False)

# GET ALL ROWS THAT MATCH A GIVEN CRITERION FROM A GIVEN LEVEL
#tractdata.xs(101.00, level=0) #Access all loans given in census tract 101
#tractdata.xs('low', level=1) # Access all loans given to low income group 
#tractdata.xs(2016, level=2) # Access all loans given in 2016
#tractdata.xs('WELLS FARGO BANK, N.A. (CA)', level=3) # Access all loans given by Wells Fargo

# FIND NUMBER OF LOANS FOR EACH VALUE AT EACH LEVEL

#tractdata.groupby([pandas.Grouper(level='census_tract')]).sum() # Number of loans given in each census tract
#tractdata.groupby([pandas.Grouper(level='cra_level')]).sum() # Number of loans given at each CRA income level
#tractdata.groupby([pandas.Grouper(level='activity_year')]).sum() # Number of loans given per year
#tractdata.groupby([pandas.Grouper(level='institution_name')]).sum() # Number of loans given per institution


# ITERATE THROUGH VALUES OF AN INDEX LEVEL AND SEE THE GROUPS FORMED BY THOSE VALUES
#grouped_by_census_tract = tractdata.groupby('census_tract')
#for name, group in grouped_by_census_tract:
#    print(name)
#    print(group)
#    print('\n')

# ACCESS A GROUP ASSOCIATED WITH A SPECIFIC VALUE ONE ONE LEVEL...
#grouped_by_census_tract = tractdata.groupby('census_tract')
#grouped_by_census_tract.get_group(101.00)
# OR ON MULTIPLE LEVELS...
#tractdata.groupby(['census_tract', 'institution_name']).get_group((101, '1ST MARINER BANK (MD)')) # note get_group takes a tuple

print('backing up...')
tractdata_bk = tractdata

print('OK')
#tractdata

retrieving from backup...
grouping...
backing up...
OK

Connect census tract aggregates with shapefiles¶

print('getting tract loan data from backup...')
tractdata = tractdata_bk

#tractdata
print('Grouping by tract only...')
grouped_by_census_tract = tractdata.groupby('census_tract').sum()

print('\nAdding loan data to shape data...')
tract_shapes_gdf = tract_shapes_gdf.assign(nLoans=grouped_by_census_tract)

#print('\nCalculating loans per 10,000 pepole...')
#tract_shapes_gdf = tract_shapes_gdf.assign(nLoansPer10k = (10000 * tract_shapes_gdf['nLoans']) / tract_shapes_gdf['population'])

print('matching up CSAs...')
tract_shapes_gdf = tract_shapes_gdf.to_crs(water_gdf.crs)

print('\nbacking up...')
tract_shapes_gdf_bk = tract_shapes_gdf

print('ok')

getting tract loan data from backup...
Grouping by tract only...

Adding loan data to shape data...
matching up CSAs...

backing up...
ok

Examine two census tracts: 1207 (Charles Village) and 1403 (Druid Heights)¶

scale = 1
fig, ax = plt.subplots(figsize=(48*scale,48*scale))
ax.set_aspect('equal')
ax.tick_params(axis='both', which='both', bottom=False, left=False, labelleft=False, labelbottom=False)

csa1 = tract_shapes_gdf[tract_shapes_gdf.index == 1207].plot(ax=ax, alpha=0.5, color='red', edgecolor='black', linewidth=2*scale)
csa2 = tract_shapes_gdf[tract_shapes_gdf.index == 1403].plot(ax=ax, alpha=0.5, color='green', edgecolor='black', linewidth=2*scale)

water_gdf[water_gdf['NAME'] == 'Harbor'].plot(ax=ax)
boundary_gdf.plot(ax=ax, color='none', edgecolor='black', linewidth=10*scale)
streets_gdf.plot(ax=ax, color='black', linewidth=0.5*scale)

#https://stackoverflow.com/questions/44098362/using-mpatches-patch-for-a-custom-legend
colors = ["r", "g"]
texts = ["Census tract 1207", "Census tract 1403"]
patches = [plt.plot([],[], marker="s", ms=100*scale, ls="", mec=None, color=colors[i], 
            label="{:s}".format(texts[i]) )[0]  for i in range(len(texts)) ]
plt.legend(handles=patches, bbox_to_anchor=(0.25, 0.15), 
           loc='center', ncol=1, numpoints=1, fontsize=80*scale, labelspacing=1*scale)
plt.title('Locator map for Baltimore census tracts', fontsize=84*scale)

plt.savefig(outdir+'figures/locator_1207_1403.svg', format='svg')
print('Figure saved')
#plt.show()

Figure saved

Number of loans by assessment area for those census tracts¶

Census tract 1207¶

loans_1207_by_assessment_area_s = pandas.Series(
    data=baltimore_tracts_df[baltimore_tracts_df['census_tract'] == 1207].fillna('none or unknown').groupby('assessment_area_number').size(), 
)
#loans_1207_by_assessment_area_s.name = 'nLoans'
loans_1207_by_assessment_area_df = pandas.DataFrame(data=loans_1207_by_assessment_area_s.values, columns=['nLoans'], index=loans_1207_by_assessment_area_s.index)
loans_1207_by_assessment_area_df

Census tract 1403¶

loans_1403_by_assessment_area_s = pandas.Series(
    data=baltimore_tracts_df[baltimore_tracts_df['census_tract'] == 1403].fillna('none or unknown').groupby('assessment_area_number').size(), 
)
loans_1403_by_assessment_area_df = pandas.DataFrame(data=loans_1403_by_assessment_area_s.values, columns=['nLoans'], index=loans_1403_by_assessment_area_s.index)
loans_1403_by_assessment_area_df

Number of loans by income group for those census tracts¶

Census tract 1207¶

loans_1207_by_income_group_s = pandas.Series(
    data=baltimore_tracts_df[baltimore_tracts_df['census_tract'] == 1207].fillna('none or unknown').groupby('income_group').size(), 
)
loans_1207_by_income_group_df = pandas.DataFrame(data=loans_1207_by_income_group_s.values, columns=['nLoans'], index=loans_1207_by_income_group_s.index)
loans_1207_by_income_group_df

Census tract 1403¶

loans_1403_by_income_group_s = pandas.Series(
    data=baltimore_tracts_df[baltimore_tracts_df['census_tract'] == 1403].fillna('none or unknown').groupby('income_group').size(), 
)
loans_1403_by_income_group_df = pandas.DataFrame(data=loans_1403_by_income_group_s.values, columns=['nLoans'], index=loans_1403_by_income_group_s.index)
loans_1403_by_income_group_df

List of all loans in those census tracts¶

Census tract 1207¶

output = baltimore_tracts_df[[
    'census_tract', 'assessment_area_number', 'income_group', 'cra_level', 'institution_name'
]][baltimore_tracts_df['census_tract'] == 1207].fillna('').sort_values('income_group')

htmlString = '<table>'
htmlString += '<tr><th>Census tract</th><th>Assessment area</th><th>Income group</th><th>CRA level</th><th>Institution name</th></tr>'
for ix, thisrow in output.iterrows():
    htmlString += '<tr>'
    for thiscol in thisrow:
        htmlString += '<td>'+str(thiscol)+'</td>'
    htmlString += '<tr>'
htmlString += '</table>'

display(HTML(htmlString))

Census tract 1403¶

output = baltimore_tracts_df[[
    'census_tract', 'assessment_area_number', 'income_group', 'cra_level', 'institution_name'
]][baltimore_tracts_df['census_tract'] == 1403].fillna('').sort_values('income_group')

htmlString = '<table>'
htmlString += '<tr><th>Census tract</th><th>Assessment area</th><th>Income group</th><th>CRA level</th><th>Institution name</th></tr>'
for ix, thisrow in output.iterrows():
    htmlString += '<tr>'
    for thiscol in thisrow:
        htmlString += '<td>'+str(thiscol)+'</td>'
    htmlString += '<tr>'
htmlString += '</table>'

display(HTML(htmlString))

Analysis by CSA¶

Aggregate by CSA¶

print('retrieving tracts data from backup...')
baltimore_tracts_df = baltimore_tracts_df_bk

print('adding CSA data...')
census_tract_to_neighborhood_df = pandas.read_csv('/home/idies/workspace/Storage/raddick/persistent/cra/metadata/census_tract_to_neighborhood.csv', low_memory=False)#, index_col='NAME10')
census_tract_to_neighborhood_df = census_tract_to_neighborhood_df.set_index('NAME10')

baltimore_tracts_df = baltimore_tracts_df.join(census_tract_to_neighborhood_df, how='left', on='census_tract')

print('backing up...')
baltimore_tracts_df_bk = baltimore_tracts_df
print('\nok')

#census_tract_to_neighborhood_df#.dtypes

print('grouping by community statistical area...')

csaloans_df = baltimore_tracts_df[baltimore_tracts_df['loan_indicator'] == 'Y'].groupby(['CSA2010', 'cra_level', 'activity_year', 'institution_name']).size()

csaloans_df = csaloans_df.sort_index()
# Run this to get every index value to display in every row:
#pandas.set_option('display.multi_sparse', False)

# GET ALL ROWS THAT MATCH A GIVEN CRITERION FROM A GIVEN LEVEL
#tractdata.xs(101.00, level=0) #Access all loans given in census tract 101
#tractdata.xs('low', level=1) # Access all loans given to low income group 
#tractdata.xs(2016, level=2) # Access all loans given in 2016
#tractdata.xs('WELLS FARGO BANK, N.A. (CA)', level=3) # Access all loans given by Wells Fargo

# FIND NUMBER OF LOANS FOR EACH VALUE AT EACH LEVEL

#tractdata.groupby([pandas.Grouper(level='census_tract')]).sum() # Number of loans given in each census tract
#tractdata.groupby([pandas.Grouper(level='cra_level')]).sum() # Number of loans given at each CRA income level
#tractdata.groupby([pandas.Grouper(level='activity_year')]).sum() # Number of loans given per year
#tractdata.groupby([pandas.Grouper(level='institution_name')]).sum() # Number of loans given per institution


# ITERATE THROUGH VALUES OF AN INDEX LEVEL AND SEE THE GROUPS FORMED BY THOSE VALUES
#grouped_by_census_tract = tractdata.groupby('census_tract')
#for name, group in grouped_by_census_tract:
#    print(name)
#    print(group)
#    print('\n')

# ACCESS A GROUP ASSOCIATED WITH A SPECIFIC VALUE ONE ONE LEVEL...
#grouped_by_census_tract = tractdata.groupby('census_tract')
#grouped_by_census_tract.get_group(101.00)
# OR ON MULTIPLE LEVELS...
#tractdata.groupby(['census_tract', 'institution_name']).get_group((101, '1ST MARINER BANK (MD)')) # note get_group takes a tuple

print('\nbacking up...')
csaloans_df_bk = csaloans_df

print('grouping number of loans by CSA...')
nloans_by_csa = csaloans_df.groupby('CSA2010').sum()
nloans_by_csa.name = 'nLoans'

print('OK')
csaloans_df.sample(2)

retrieving tracts data from backup...
adding CSA data...
backing up...

ok
grouping by community statistical area...

backing up...
grouping number of loans by CSA...
OK

CSA2010      cra_level  activity_year  institution_name        
Hamilton     moderate   2007           SUNTRUST BANKS, INC (GA)    1
Cherry Hill  low        2015           Chase Bank USA, NA (DE)     1
dtype: int64

Get population data for neighborhoods from their census tracts¶

print('Adding CSA names to tract geodata...')
census_tract_to_neighborhood_df = pandas.read_csv('/home/idies/workspace/Storage/raddick/persistent/cra/metadata/census_tract_to_neighborhood.csv', low_memory=False)
census_tract_to_neighborhood_df['NAME10'] = pandas.to_numeric(census_tract_to_neighborhood_df['NAME10'], errors='coerce')
census_tract_to_neighborhood_df = census_tract_to_neighborhood_df.set_index('NAME10')

tract_shapes_with_csa_gdf = tract_shapes_gdf.join(census_tract_to_neighborhood_df, how='left')

csa_gdf = tract_shapes_with_csa_gdf.groupby('CSA2010')[['white', 'population']].sum()

csa_gdf.sample(2)

Adding CSA names to tract geodata...

Get CSA shapefiles¶

print('We have tract-based population data for {0:,.0f} CSAs, and CSA-based loan data for {1:,.0f}'.format(len(csa_gdf), len(nloans_by_csa)))

print('\nCombining census tract geometries into CSA geometries...')
csa_gdf = csa_gdf.assign(geometry='')
for thiscsa, thisrow in csa_gdf.iterrows():
    tracts_here = tract_shapes_with_csa_gdf[tract_shapes_with_csa_gdf['CSA2010'] == thiscsa].index.values.tolist()
    geos_here = tract_shapes_with_csa_gdf[tract_shapes_with_csa_gdf.index.isin(tracts_here)].geometry
    csa_gdf.loc[thiscsa, 'geometry'] = geos_here.unary_union
    
csa_gdf = csa_gdf.set_geometry('geometry')

print('\nAdding loan data...')
csa_gdf = csa_gdf.join(nloans_by_csa, how='left')

print('Calculating percent white...')
csa_gdf = csa_gdf.assign(percent_white = csa_gdf['white'] / csa_gdf['population'])

print('backing up...')
csa_gdf_bk = csa_gdf

csa_gdf.sample(2)

We have tract-based population data for 56 CSAs, and CSA-based loan data for 56

Combining census tract geometries into CSA geometries...

Adding loan data...
Calculating percent white...
backing up...

Analyze loans per neighborhood business¶

# Get CSA data from backup
csa_gdf = csa_gdf_bk

business_data_dir = '/home/idies/workspace/Storage/raddick/Baltimore/community_reinvestment_act/mfi_and_business_data/'
businesses_by_csa_df = pandas.read_excel(business_data_dir+'Vital Signs Topic Area Workforce And Economic Development.xlsx')

businesses_by_csa_df = businesses_by_csa_df.rename(columns={'Community': 'CSA2010'})
businesses_by_csa_df = businesses_by_csa_df.set_index('CSA2010')
businesses_by_csa_df = businesses_by_csa_df.rename(columns={'2016 Data': 'businesses_per_1k_residents'})

csa_gdf = csa_gdf.join(businesses_by_csa_df)

csa_gdf = csa_gdf.assign(total_businesses = np.round(csa_gdf['businesses_per_1k_residents'] * csa_gdf['population'] / 1000,0))
csa_gdf = csa_gdf.assign(loans_per_business = csa_gdf['nLoans'] / csa_gdf['total_businesses'])

print('backing up...')
#csa_gdf_bk = csa_gdf
csa_gdf.head(1)

backing up...

Examine two CSAs: GCVB and UDH¶

Locator map of Greater Charles Village/Barclay and Upton/Druid Heights¶

scale = 1#$0.25
fig, ax = plt.subplots(figsize=(48*scale,48*scale))
ax.set_aspect('equal')
ax.tick_params(axis='both', which='both', bottom=False, left=False, labelleft=False, labelbottom=False)

csa1 = csa_gdf[csa_gdf.index == 'Greater Charles Village/Barclay'].plot(ax=ax, alpha=0.5, color='red', edgecolor='black', linewidth=2*scale)
csa2 = csa_gdf[csa_gdf.index == 'Upton/Druid Heights'].plot(ax=ax, alpha=0.5, color='green', edgecolor='black', linewidth=2*scale)

#csa_gdf.plot(ax=ax, alpha=0.15, color='red', edgecolor='black', linewidth=8*scale)

water_gdf[water_gdf['NAME'] == 'Harbor'].plot(ax=ax)

boundary_gdf.plot(ax=ax, color='none', edgecolor='black', linewidth=10*scale)

streets_gdf.plot(ax=ax, color='black', linewidth=0.5*scale)

#for ix, thisrow in csa_gdf[csa_gdf['population'].notnull()].iterrows():
#    annotator = str(ix)
#    annotator = annotator.replace('/','/\n')
#    ax.annotate(annotator, 
#                xy=(thisrow.geometry.centroid.x, thisrow.geometry.centroid.y), 
#                xytext=(thisrow.geometry.centroid.x, thisrow.geometry.centroid.y), 
#                ha='center', va='center', backgroundcolor='white', fontsize=20*scale)

#https://stackoverflow.com/questions/44098362/using-mpatches-patch-for-a-custom-legend
colors = ["r", "g"]
texts = ["Greater Charles Village /\nBarclay", "Upton /\nDruid Heights"]
patches = [plt.plot([],[], marker="s", ms=100*scale, ls="", mec=None, color=colors[i], 
            label="{:s}".format(texts[i]) )[0]  for i in range(len(texts)) ]
plt.legend(handles=patches, bbox_to_anchor=(0.25, 0.15), 
           loc='center', ncol=1, numpoints=1, fontsize=64*scale, labelspacing=1*scale)
plt.title('Locator map for Baltimore community statistical areas', fontsize=80*scale)
    
#plt.show()
plt.savefig(outdir+'figures/locator_gcvb_dhu.svg', format='svg')
print('Figure saved!')

Figure saved!

Loans by income group for these CSAs¶

Greater Charles Village/Barclay¶

print('{0:} has {1:,.0f} people and {2:,.0f} businesses.'.format('Greater Charles Village/Barclay', csa_gdf['population'].loc['Greater Charles Village/Barclay'], csa_gdf['total_businesses'].loc['Greater Charles Village/Barclay']))
print('{0:} received {1:,.0f} CRA loans, which is {2:.2f} per business.'.format('Greater Charles Village/Barclay', csa_gdf['nLoans'].loc['Greater Charles Village/Barclay'], csa_gdf['loans_per_business'].loc['Greater Charles Village/Barclay']))

loans_gcvb_by_income_group_s = pandas.Series(
    data=baltimore_tracts_df[baltimore_tracts_df['CSA2010'] == 'Greater Charles Village/Barclay'].fillna('none or unknown').groupby('income_group').size(), 
)
loans_gcvb_by_income_group_df = pandas.DataFrame(data=loans_gcvb_by_income_group_s.values, columns=['nLoans'], index=loans_gcvb_by_income_group_s.index)
loans_gcvb_by_income_group_df

Greater Charles Village/Barclay has 16,391 people and 610 businesses.
Greater Charles Village/Barclay received 656 CRA loans, which is 1.08 per business.

Upton / Druid Heights¶

print('{0:} has {1:,.0f} people and {2:,.0f} businesses.'.format('Upton / Druid Heights', csa_gdf['population'].loc['Upton/Druid Heights'], csa_gdf['total_businesses'].loc['Upton/Druid Heights']))
print('{0:} received {1:,.0f} CRA loans, which is {2:.2f} per business.'.format('Upton / Druid Heights', csa_gdf['nLoans'].loc['Upton/Druid Heights'], csa_gdf['loans_per_business'].loc['Upton/Druid Heights']))

loans_udh_by_income_group_s = pandas.Series(
    data=baltimore_tracts_df[baltimore_tracts_df['CSA2010'] == 'Upton/Druid Heights'].fillna('none or unknown').groupby('income_group').size(), 
)
loans_udh_by_income_group_df = pandas.DataFrame(data=loans_udh_by_income_group_s.values, columns=['nLoans'], index=loans_udh_by_income_group_s.index)
loans_udh_by_income_group_df

Upton / Druid Heights has 10,342 people and 144 businesses.
Upton / Druid Heights received 327 CRA loans, which is 2.27 per business.

List of all loans in those census tracts¶

Greater Charles Village / Barclay¶

output = baltimore_tracts_df[[
    'CSA2010', 'income_group', 'cra_level', 'institution_name'
]][baltimore_tracts_df['CSA2010'] == 'Greater Charles Village/Barclay'].fillna('').sort_values('income_group')

htmlString = '<table>'
htmlString += '<tr><th>CSA</th><th>Income group</th><th>CRA level</th><th>Institution name</th></tr>'
for ix, thisrow in output.iterrows():
    htmlString += '<tr>'
    for thiscol in thisrow:
        htmlString += '<td>'+str(thiscol)+'</td>'
    htmlString += '<tr>'
htmlString += '</table>'

display(HTML(htmlString))

Upton / Druid Heights¶

output = baltimore_tracts_df[[
    'CSA2010', 'income_group', 'cra_level', 'institution_name'
]][baltimore_tracts_df['CSA2010'] == 'Upton/Druid Heights'].fillna('').sort_values('income_group')

htmlString = '<table>'
htmlString += '<tr><th>CSA</th><th>Income group</th><th>CRA level</th><th>Institution name</th></tr>'
for ix, thisrow in output.iterrows():
    htmlString += '<tr>'
    for thiscol in thisrow:
        htmlString += '<td>'+str(thiscol)+'</td>'
    htmlString += '<tr>'
htmlString += '</table>'

display(HTML(htmlString))

	nLoans
assessment_area_number
1.0	22
2.0	1
5.0	2
6.0	1
7.0	2
8.0	1
9.0	2
13.0	3
18.0	1
19.0	6
25.0	1
26.0	1
30.0	1
47.0	1
51.0	11
53.0	1
71.0	4
73.0	2
74.0	2
78.0	1
84.0	1
109.0	1
112.0	1
129.0	2
131.0	2
173.0	1
176.0	1
177.0	1
180.0	1
4001.0	1
none or unknown	140

	nLoans
assessment_area_number
1.0	5
6.0	2
9.0	1
13.0	2
19.0	2
30.0	1
51.0	4
57.0	1
71.0	3
73.0	2
84.0	1
110.0	1
112.0	1
131.0	1
173.0	1
174.0	1
176.0	1
177.0	1
180.0	1
1001.0	1
2401.0	1
4001.0	1
none or unknown	87

Census tract	Assessment area	Income group	CRA level	Institution name
1403.0	177.0	40% to 50% of MFI	low	WELLS FARGO BANK, N.A. (CA)
1403.0	13.0	40% to 50% of MFI	low	Capital One, N.A. (VA)
1403.0		40% to 50% of MFI	low	FIRST NATIONAL BANK OF OMAHA (NE)
1403.0	174.0	40% to 50% of MFI	low	WELLS FARGO BANK, N.A. (CA)
1403.0		40% to 50% of MFI	low	American Express, FSB (UT)
1403.0		40% to 50% of MFI	low	Synchrony Bank (UT)
1403.0		40% to 50% of MFI	low	Unknown
1403.0		40% to 50% of MFI	low	Unknown
1403.0	131.0	40% to 50% of MFI	low	WELLS FARGO BANK, N.A. (CA)
1403.0		40% to 50% of MFI	low	Unknown
1403.0		40% to 50% of MFI	low	Unknown
1403.0	1.0	40% to 50% of MFI	low	1ST MARINER BANK (MD)
1403.0		40% to 50% of MFI	low	Unknown
1403.0		40% to 50% of MFI	low	Unknown
1403.0		40% to 50% of MFI	low	Unknown
1403.0	2401.0	40% to 50% of MFI	low	WELLS FARGO BANK, N.A. (CA)
1403.0		40% to 50% of MFI	low	Unknown
1403.0		40% to 50% of MFI	low	Unknown
1403.0		40% to 50% of MFI	low	Capital One Bank (USA), N.A. (VA)
1403.0		40% to 50% of MFI	low	Chase Bank USA, NA (DE)
1403.0		40% to 50% of MFI	low	Unknown
1403.0	110.0	40% to 50% of MFI	low	WELLS FARGO BANK, N.A. (CA)
1403.0		40% to 50% of MFI	low	Unknown
1403.0		40% to 50% of MFI	low	Unknown
1403.0		40% to 50% of MFI	low	Unknown
1403.0		40% to 50% of MFI	low	Unknown
1403.0	19.0	40% to 50% of MFI	low	Northwest Bank (PA)
1403.0		40% to 50% of MFI	low	Unknown
1403.0		40% to 50% of MFI	low	Unknown
1403.0		40% to 50% of MFI	low	Capital One Bank (USA), N.A. (VA)
1403.0		40% to 50% of MFI	low	Synchrony Bank (UT)
1403.0	6.0	40% to 50% of MFI	low	SUNTRUST BANKS, INC (GA)
1403.0	51.0	40% to 50% of MFI	low	M&T BANK (NY)
1403.0		40% to 50% of MFI	low	American Express, FSB (UT)
1403.0		40% to 50% of MFI	low	Synchrony Bank (UT)
1403.0		40% to 50% of MFI	low	Capital One Bank (USA), N.A. (VA)
1403.0		40% to 50% of MFI	low	TEXAS CAPITAL BANK (TX)
1403.0		40% to 50% of MFI	low	CITIBANK, N.A. (NY)
1403.0	84.0	40% to 50% of MFI	low	PNC BANK N.A. (PA)
1403.0	180.0	40% to 50% of MFI	low	WELLS FARGO BANK, N.A. (CA)
1403.0	57.0	40% to 50% of MFI	low	SUNTRUST BANKS, INC (GA)
1403.0	51.0	40% to 50% of MFI	low	M&T BANK (NY)
1403.0		40% to 50% of MFI	low	Synchrony Bank (UT)
1403.0		40% to 50% of MFI	low	Capital One Bank (USA), N.A. (VA)
1403.0		40% to 50% of MFI	low	TEXAS CAPITAL BANK (TX)
1403.0		40% to 50% of MFI	low	Unknown
1403.0	1.0	40% to 50% of MFI	low	Bank of America, N.A. (CA)
1403.0	176.0	40% to 50% of MFI	low	WELLS FARGO BANK, N.A. (CA)
1403.0	73.0	40% to 50% of MFI	low	SUNTRUST BANKS, INC (GA)
1403.0	51.0	40% to 50% of MFI	low	M&T BANK (NY)
1403.0		40% to 50% of MFI	low	American Express, FSB (UT)
1403.0		40% to 50% of MFI	low	Synchrony Bank (UT)
1403.0		40% to 50% of MFI	low	Capital One Bank (USA), N.A. (VA)
1403.0		40% to 50% of MFI	low	Unknown
1403.0		40% to 50% of MFI	low	FIRST NATIONAL BANK OF OMAHA (NE)
1403.0	173.0	40% to 50% of MFI	low	WELLS FARGO BANK, N.A. (CA)
1403.0		40% to 50% of MFI	low	Sandy Spring Bank (MD)
1403.0		40% to 50% of MFI	low	American Express, FSB (UT)
1403.0	73.0	40% to 50% of MFI	low	SUNTRUST BANKS, INC (GA)
1403.0		40% to 50% of MFI	low	Capital One Bank (USA), N.A. (VA)
1403.0		40% to 50% of MFI	low	Chase Bank USA, NA (DE)
1403.0		40% to 50% of MFI	low	Unknown
1403.0		40% to 50% of MFI	low	Unknown
1403.0	71.0	40% to 50% of MFI	low	SUNTRUST BANKS, INC (GA)
1403.0	51.0	40% to 50% of MFI	low	M&T BANK (NY)
1403.0		40% to 50% of MFI	low	Unknown
1403.0		40% to 50% of MFI	low	Unknown
1403.0		40% to 50% of MFI	low	Unknown
1403.0	13.0	40% to 50% of MFI	low	WELLS FARGO BANK, N.A. (CA)
1403.0		40% to 50% of MFI	low	Unknown
1403.0		40% to 50% of MFI	low	Unknown
1403.0	1001.0	40% to 50% of MFI	low	1ST MARINER BANK (MD)
1403.0		40% to 50% of MFI	low	Unknown
1403.0		40% to 50% of MFI	low	Unknown
1403.0		40% to 50% of MFI	low	Unknown
1403.0		40% to 50% of MFI	low	Unknown
1403.0		40% to 50% of MFI	low	Unknown
1403.0	4001.0	40% to 50% of MFI	low	WELLS FARGO BANK, N.A. (CA)
1403.0		40% to 50% of MFI	low	AMERIS BANK (GA)
1403.0		40% to 50% of MFI	low	Lake Forest Bank and Trust (IL)
1403.0	6.0	40% to 50% of MFI	low	SUNTRUST BANKS, INC (GA)
1403.0		40% to 50% of MFI	low	American Express, FSB (UT)
1403.0		40% to 50% of MFI	low	Synchrony Bank (UT)
1403.0		40% to 50% of MFI	low	MetaBank (IA)
1403.0		40% to 50% of MFI	low	Capital One Bank (USA), N.A. (VA)
1403.0		40% to 50% of MFI	low	Barrington Bank and Trust (IL)
1403.0	1.0	40% to 50% of MFI	low	Bank of America, N.A. (CA)
1403.0		40% to 50% of MFI	low	CITIBANK, N.A. (NY)
1403.0	71.0	40% to 50% of MFI	low	PNC BANK N.A. (PA)
1403.0	1.0	40% to 50% of MFI	low	Unknown
1403.0	9.0	40% to 50% of MFI	low	Unknown
1403.0		40% to 50% of MFI	low	Unknown
1403.0		40% to 50% of MFI	low	Unknown
1403.0		40% to 50% of MFI	low	Unknown
1403.0		40% to 50% of MFI	low	Unknown
1403.0	30.0	40% to 50% of MFI	low	PNC BANK N.A. (PA)
1403.0	112.0	40% to 50% of MFI	low	WELLS FARGO BANK, N.A. (CA)
1403.0		40% to 50% of MFI	low	Unknown
1403.0		40% to 50% of MFI	low	Unknown
1403.0		40% to 50% of MFI	low	Unknown
1403.0		40% to 50% of MFI	low	Unknown
1403.0		40% to 50% of MFI	low	Unknown
1403.0	19.0	40% to 50% of MFI	low	Northwest Bank (PA)
1403.0	1.0	40% to 50% of MFI	low	Unknown
1403.0	71.0	40% to 50% of MFI	low	SUNTRUST BANKS, INC (GA)
1403.0		40% to 50% of MFI	low	Sandy Spring Bank (MD)
1403.0		40% to 50% of MFI	low	Unknown
1403.0		40% to 50% of MFI	low	Chase Bank USA, NA (DE)
1403.0		40% to 50% of MFI	low	Unknown
1403.0		40% to 50% of MFI	low	Unknown
1403.0		40% to 50% of MFI	low	Unknown
1403.0		40% to 50% of MFI	low	Unknown
1403.0		40% to 50% of MFI	low	Unknown
1403.0		40% to 50% of MFI	low	Unknown
1403.0		40% to 50% of MFI	low	Unknown
1403.0		40% to 50% of MFI	low	Unknown
1403.0		40% to 50% of MFI	low	Unknown
1403.0		40% to 50% of MFI	low	Unknown
1403.0		40% to 50% of MFI	low	Unknown
1403.0		40% to 50% of MFI	low	Unknown
1403.0		40% to 50% of MFI	low	Unknown
1403.0		40% to 50% of MFI	low	Unknown

	white	population
CSA2010
South Baltimore	5897.0	6406.0
Clifton-Berea	116.0	9874.0

	white	population	geometry	nLoans	percent_white
CSA2010
Unassigned--Jail	809.0	5237.0	POLYGON ((1422260.725789612 595529.1030492543,...	4	0.154478
Beechfield/Ten Hills/West Hills	2112.0	12264.0	POLYGON ((1399042.70455329 588235.8089164203, ...	354	0.172211

Census tract	Assessment area	Income group	CRA level	Institution name
1207.0		40% to 50% of MFI	low	Unknown
1207.0		40% to 50% of MFI	low	Chase Bank USA, NA (DE)
1207.0		40% to 50% of MFI	low	Capital One Bank (USA), N.A. (VA)
1207.0		40% to 50% of MFI	low	Unknown
1207.0	51.0	40% to 50% of MFI	low	M&T BANK (NY)
1207.0		40% to 50% of MFI	low	Unknown
1207.0	1.0	40% to 50% of MFI	low	Unknown
1207.0	19.0	40% to 50% of MFI	low	Northwest Bank (PA)
1207.0		40% to 50% of MFI	low	Unknown
1207.0		40% to 50% of MFI	low	Unknown
1207.0		40% to 50% of MFI	low	JPMorgan Chase Bank NA (OH)
1207.0	129.0	40% to 50% of MFI	low	PNC BANK N.A. (PA)
1207.0	1.0	40% to 50% of MFI	low	Bank of America, N.A. (CA)
1207.0		40% to 50% of MFI	low	Unknown
1207.0		40% to 50% of MFI	low	Unknown
1207.0		40% to 50% of MFI	low	Chase Bank USA, NA (DE)
1207.0		40% to 50% of MFI	low	Unknown
1207.0		40% to 50% of MFI	low	Unknown
1207.0	19.0	40% to 50% of MFI	low	Bank of America, N.A. (CA)
1207.0	30.0	40% to 50% of MFI	low	PNC BANK N.A. (PA)
1207.0	109.0	40% to 50% of MFI	low	WELLS FARGO BANK, N.A. (CA)
1207.0		40% to 50% of MFI	low	FIRST NATIONAL BANK OF OMAHA (NE)
1207.0	5.0	40% to 50% of MFI	low	PNC BANK N.A. (PA)
1207.0	19.0	40% to 50% of MFI	low	Bank of America, N.A. (CA)
1207.0		40% to 50% of MFI	low	Unknown
1207.0		40% to 50% of MFI	low	Unknown
1207.0		40% to 50% of MFI	low	Chase Bank USA, NA (DE)
1207.0		40% to 50% of MFI	low	TD Bank N.A. (DE)
1207.0	71.0	40% to 50% of MFI	low	SUNTRUST BANKS, INC (GA)
1207.0		40% to 50% of MFI	low	Unknown
1207.0		40% to 50% of MFI	low	Unknown
1207.0		40% to 50% of MFI	low	Unknown
1207.0		40% to 50% of MFI	low	Unknown
1207.0		40% to 50% of MFI	low	Unknown
1207.0		40% to 50% of MFI	low	Unknown
1207.0	112.0	40% to 50% of MFI	low	WELLS FARGO BANK, N.A. (CA)
1207.0	1.0	40% to 50% of MFI	low	Unknown
1207.0		40% to 50% of MFI	low	Unknown
1207.0		40% to 50% of MFI	low	Capital One Bank (USA), N.A. (VA)
1207.0		40% to 50% of MFI	low	Sandy Spring Bank (MD)
1207.0		40% to 50% of MFI	low	Unknown
1207.0	131.0	40% to 50% of MFI	low	WELLS FARGO BANK, N.A. (CA)
1207.0		40% to 50% of MFI	low	FIRST NATIONAL BANK OF OMAHA (NE)
1207.0	18.0	40% to 50% of MFI	low	PNC BANK N.A. (PA)
1207.0	1.0	40% to 50% of MFI	low	Bank of America, N.A. (CA)
1207.0	13.0	40% to 50% of MFI	low	Capital One, N.A. (VA)
1207.0		40% to 50% of MFI	low	Unknown
1207.0		40% to 50% of MFI	low	Unknown
1207.0		40% to 50% of MFI	low	Chase Bank USA, NA (DE)
1207.0		40% to 50% of MFI	low	HSBC BANK, NA (VA)
1207.0		40% to 50% of MFI	low	Capital One Bank (USA), N.A. (VA)
1207.0		40% to 50% of MFI	low	Synchrony Bank (UT)
1207.0		40% to 50% of MFI	low	American Express, FSB (UT)
1207.0	51.0	40% to 50% of MFI	low	M&T BANK (NY)
1207.0	74.0	40% to 50% of MFI	low	SUNTRUST BANKS, INC (GA)
1207.0		40% to 50% of MFI	low	Unknown
1207.0		40% to 50% of MFI	low	HOWARD BANK (MD)
1207.0		40% to 50% of MFI	low	Unknown
1207.0		40% to 50% of MFI	low	Unknown
1207.0	73.0	40% to 50% of MFI	low	SUNTRUST BANKS, INC (GA)
1207.0	1.0	40% to 50% of MFI	low	Unknown
1207.0		40% to 50% of MFI	low	Unknown
1207.0		40% to 50% of MFI	low	Unknown
1207.0		40% to 50% of MFI	low	Unknown
1207.0		40% to 50% of MFI	low	Unknown
1207.0		40% to 50% of MFI	low	Unknown
1207.0	51.0	40% to 50% of MFI	low	M&T BANK (NY)
1207.0	47.0	40% to 50% of MFI	low	PNC BANK N.A. (PA)
1207.0		40% to 50% of MFI	low	Unknown
1207.0		40% to 50% of MFI	low	Unknown
1207.0		40% to 50% of MFI	low	Chase Bank USA, NA (DE)
1207.0		40% to 50% of MFI	low	Capital One Bank (USA), N.A. (VA)
1207.0	51.0	40% to 50% of MFI	low	M&T BANK (NY)
1207.0		40% to 50% of MFI	low	Sandy Spring Bank (MD)
1207.0	74.0	40% to 50% of MFI	low	SUNTRUST BANKS, INC (GA)
1207.0	1.0	40% to 50% of MFI	low	Bank of America, N.A. (CA)
1207.0		40% to 50% of MFI	low	Unknown
1207.0	51.0	40% to 50% of MFI	low	M&T BANK (NY)
1207.0		40% to 50% of MFI	low	Unknown
1207.0		40% to 50% of MFI	low	Chase Bank USA, NA (DE)
1207.0		40% to 50% of MFI	low	Unknown
1207.0	19.0	40% to 50% of MFI	low	Bank of America, N.A. (CA)
1207.0		40% to 50% of MFI	low	Unknown
1207.0		40% to 50% of MFI	low	Unknown
1207.0	13.0	40% to 50% of MFI	low	WELLS FARGO BANK, N.A. (CA)
1207.0		40% to 50% of MFI	low	Unknown
1207.0		40% to 50% of MFI	low	WEX BANK (UT)
1207.0		40% to 50% of MFI	low	Unknown
1207.0		40% to 50% of MFI	low	Unknown
1207.0		40% to 50% of MFI	low	Unknown
1207.0		40% to 50% of MFI	low	American Express Centurion Bk (UT)
1207.0	1.0	40% to 50% of MFI	low	Unknown
1207.0	1.0	40% to 50% of MFI	low	Unknown
1207.0		40% to 50% of MFI	low	Unknown
1207.0	53.0	40% to 50% of MFI	low	SUNTRUST BANKS, INC (GA)
1207.0		40% to 50% of MFI	low	Unknown
1207.0		40% to 50% of MFI	low	Unknown
1207.0		40% to 50% of MFI	low	Unknown
1207.0		40% to 50% of MFI	low	Unknown
1207.0		40% to 50% of MFI	low	Chase Bank USA, NA (DE)
1207.0		40% to 50% of MFI	low	Unknown
1207.0	19.0	40% to 50% of MFI	low	Bank of America, N.A. (CA)
1207.0		40% to 50% of MFI	low	Unknown
1207.0	4001.0	40% to 50% of MFI	low	WELLS FARGO BANK, N.A. (CA)
1207.0		40% to 50% of MFI	low	Unknown
1207.0		40% to 50% of MFI	low	Unknown
1207.0	51.0	40% to 50% of MFI	low	M&T BANK (NY)
1207.0	51.0	40% to 50% of MFI	low	M&T BANK (NY)
1207.0		40% to 50% of MFI	low	Unknown
1207.0		40% to 50% of MFI	low	Unknown
1207.0		40% to 50% of MFI	low	Unknown
1207.0		40% to 50% of MFI	low	Unknown
1207.0		40% to 50% of MFI	low	Unknown
1207.0	1.0	40% to 50% of MFI	low	Unknown
1207.0	5.0	40% to 50% of MFI	low	Unknown
1207.0	51.0	40% to 50% of MFI	low	M&T BANK (NY)
1207.0		40% to 50% of MFI	low	Unknown
1207.0		40% to 50% of MFI	low	Unknown
1207.0		40% to 50% of MFI	low	Chase Bank USA, NA (DE)
1207.0		40% to 50% of MFI	low	Unknown
1207.0		40% to 50% of MFI	low	Unknown
1207.0	1.0	40% to 50% of MFI	low	Unknown
1207.0	19.0	40% to 50% of MFI	low	Bank of America, N.A. (CA)
1207.0		40% to 50% of MFI	low	Unknown
1207.0	71.0	40% to 50% of MFI	low	SUNTRUST BANKS, INC (GA)
1207.0		40% to 50% of MFI	low	Unknown
1207.0	1.0	40% to 50% of MFI	low	Unknown
1207.0		40% to 50% of MFI	low	Unknown
1207.0	1.0	40% to 50% of MFI	low	Unknown
1207.0		40% to 50% of MFI	low	Unknown
1207.0		40% to 50% of MFI	low	Unknown
1207.0	78.0	40% to 50% of MFI	low	WELLS FARGO BANK, N.A. (CA)
1207.0		40% to 50% of MFI	low	Unknown
1207.0		40% to 50% of MFI	low	Unknown
1207.0		50% to 60% of MFI	moderate	TEXAS CAPITAL BANK (TX)
1207.0	71.0	50% to 60% of MFI	moderate	PNC BANK N.A. (PA)
1207.0		50% to 60% of MFI	moderate	Capital One Bank (USA), N.A. (VA)
1207.0		50% to 60% of MFI	moderate	Chase Bank USA, NA (DE)
1207.0		50% to 60% of MFI	moderate	Unknown
1207.0	1.0	50% to 60% of MFI	moderate	Bank of America, N.A. (CA)
1207.0	1.0	50% to 60% of MFI	moderate	CITIBANK, N.A. (NY)
1207.0		50% to 60% of MFI	moderate	US BANK NA (WI)
1207.0	73.0	50% to 60% of MFI	moderate	SUNTRUST BANKS, INC (GA)
1207.0		50% to 60% of MFI	moderate	Unknown
1207.0		50% to 60% of MFI	moderate	Capital One Bank (USA), N.A. (VA)
1207.0		50% to 60% of MFI	moderate	WEX BANK (UT)
1207.0	8.0	50% to 60% of MFI	moderate	Unknown
1207.0	51.0	50% to 60% of MFI	moderate	M&T BANK (NY)
1207.0		50% to 60% of MFI	moderate	American Express, FSB (UT)
1207.0		50% to 60% of MFI	moderate	WEX BANK (UT)
1207.0		50% to 60% of MFI	moderate	Synchrony Bank (UT)
1207.0	176.0	50% to 60% of MFI	moderate	WELLS FARGO BANK, N.A. (CA)
1207.0	7.0	50% to 60% of MFI	moderate	Northwest Bank (PA)
1207.0		50% to 60% of MFI	moderate	Banner Bank (WA)
1207.0		50% to 60% of MFI	moderate	American Express, FSB (UT)
1207.0		50% to 60% of MFI	moderate	Synchrony Bank (UT)
1207.0		50% to 60% of MFI	moderate	Chase Bank USA, NA (DE)
1207.0		50% to 60% of MFI	moderate	American Express, FSB (UT)
1207.0	51.0	50% to 60% of MFI	moderate	M&T BANK (NY)
1207.0		50% to 60% of MFI	moderate	Sandy Spring Bank (MD)
1207.0		50% to 60% of MFI	moderate	Unknown
1207.0	1.0	50% to 60% of MFI	moderate	The Columbia Bank (MD)
1207.0		50% to 60% of MFI	moderate	WEX BANK (UT)
1207.0	9.0	50% to 60% of MFI	moderate	Unknown
1207.0		50% to 60% of MFI	moderate	HOWARD BANK (MD)
1207.0		50% to 60% of MFI	moderate	US BANK NA (WI)
1207.0	84.0	50% to 60% of MFI	moderate	PNC BANK N.A. (PA)
1207.0		50% to 60% of MFI	moderate	CITIBANK, N.A. (NY)
1207.0	1.0	50% to 60% of MFI	moderate	Bank of America, N.A. (CA)
1207.0	25.0	50% to 60% of MFI	moderate	Capital One, N.A. (VA)
1207.0		50% to 60% of MFI	moderate	TCF National Bank (SD)
1207.0		50% to 60% of MFI	moderate	Capital One Bank (USA), N.A. (VA)
1207.0		50% to 60% of MFI	moderate	Synchrony Bank (UT)
1207.0	180.0	50% to 60% of MFI	moderate	WELLS FARGO BANK, N.A. (CA)
1207.0		50% to 60% of MFI	moderate	Unknown
1207.0	177.0	50% to 60% of MFI	moderate	WELLS FARGO BANK, N.A. (CA)
1207.0	1.0	50% to 60% of MFI	moderate	CITIBANK, N.A. (NY)
1207.0		50% to 60% of MFI	moderate	ALLY BANK (UT)
1207.0	6.0	50% to 60% of MFI	moderate	SUNTRUST BANKS, INC (GA)
1207.0	51.0	50% to 60% of MFI	moderate	M&T BANK (NY)
1207.0		50% to 60% of MFI	moderate	Bofi Federal Bank (CA)
1207.0		50% to 60% of MFI	moderate	American Express, FSB (UT)
1207.0		50% to 60% of MFI	moderate	Synchrony Bank (UT)
1207.0		50% to 60% of MFI	moderate	Capital One Bank (USA), N.A. (VA)
1207.0		50% to 60% of MFI	moderate	Citizens Bank, NA (RI)
1207.0		50% to 60% of MFI	moderate	TEXAS CAPITAL BANK (TX)
1207.0		50% to 60% of MFI	moderate	Barrington Bank and Trust (IL)
1207.0		50% to 60% of MFI	moderate	Chase Bank USA, NA (DE)
1207.0	1.0	50% to 60% of MFI	moderate	Bank of America, N.A. (CA)
1207.0		50% to 60% of MFI	moderate	CITIBANK, N.A. (NY)
1207.0	71.0	50% to 60% of MFI	moderate	PNC BANK N.A. (PA)
1207.0	1.0	50% to 60% of MFI	moderate	FIRST NATIONAL BANK OF PA (PA)
1207.0		50% to 60% of MFI	moderate	US BANK NA (WI)
1207.0		50% to 60% of MFI	moderate	JPMorgan Chase Bank NA (OH)
1207.0	9.0	50% to 60% of MFI	moderate	Unknown
1207.0	1.0	50% to 60% of MFI	moderate	The Columbia Bank (MD)
1207.0		50% to 60% of MFI	moderate	WEX BANK (UT)
1207.0	2.0	50% to 60% of MFI	moderate	HOWARD BANK (MD)
1207.0	131.0	50% to 60% of MFI	moderate	PNC BANK N.A. (PA)
1207.0		50% to 60% of MFI	moderate	FIRST NATIONAL BANK OF OMAHA (NE)
1207.0		50% to 60% of MFI	moderate	US BANK NA (WI)
1207.0		50% to 60% of MFI	moderate	JPMorgan Chase Bank NA (OH)
1207.0	173.0	50% to 60% of MFI	moderate	WELLS FARGO BANK, N.A. (CA)
1207.0		50% to 60% of MFI	moderate	HOWARD BANK (MD)
1207.0		50% to 60% of MFI	moderate	American Express, FSB (UT)
1207.0		50% to 60% of MFI	moderate	Capital One Bank (USA), N.A. (VA)
1207.0	1.0	50% to 60% of MFI	moderate	Bank of America, N.A. (CA)
1207.0		50% to 60% of MFI	moderate	HSBC BANK, NA (VA)
1207.0		50% to 60% of MFI	moderate	Chase Bank USA, NA (DE)
1207.0		50% to 60% of MFI	moderate	Unknown
1207.0	13.0	50% to 60% of MFI	moderate	Capital One, N.A. (VA)
1207.0	26.0	50% to 60% of MFI	moderate	Bank of America, N.A. (CA)
1207.0	1.0	50% to 60% of MFI	moderate	CITIBANK, N.A. (NY)
1207.0	129.0	50% to 60% of MFI	moderate	PNC BANK N.A. (PA)
1207.0		50% to 60% of MFI	moderate	JPMorgan Chase Bank NA (OH)
1207.0	7.0	50% to 60% of MFI	moderate	HOWARD BANK (MD)
1207.0		50% to 60% of MFI	moderate	Unknown
1207.0		50% to 60% of MFI	moderate	Unknown

	white	population	geometry	nLoans	percent_white	businesses_per_1k_residents	total_businesses	loans_per_business
CSA2010
Allendale/Irvington/S. Hilton	1409.0	16217.0	POLYGON ((1404942.473027696 592901.001299308, ...	558	0.086884	8.3	135.0	4.133333

	nLoans
income_group
30% to 40% of MFI	109
40% to 50% of MFI	96
50% to 60% of MFI	111
60% to 70% of MFI	170
70% to 80% of MFI	117
> 120% of MFI	53

	nLoans
income_group
10% to 20% of MFI	31
20% to 30% of MFI	119
30% to 40% of MFI	36
40% to 50% of MFI	122
< 10% of Median Family Income (MFI)	19

CSA	Income group	CRA level	Institution name
Upton/Druid Heights	10% to 20% of MFI	low	WELLS FARGO BANK, N.A. (CA)
Upton/Druid Heights	10% to 20% of MFI	low	American Express, FSB (UT)
Upton/Druid Heights	10% to 20% of MFI	low	CITIBANK, N.A. (NY)
Upton/Druid Heights	10% to 20% of MFI	low	Lake Forest Bank and Trust (IL)
Upton/Druid Heights	10% to 20% of MFI	low	US BANK NA (WI)
Upton/Druid Heights	10% to 20% of MFI	low	WELLS FARGO BANK, N.A. (CA)
Upton/Druid Heights	10% to 20% of MFI	low	American Express, FSB (UT)
Upton/Druid Heights	10% to 20% of MFI	low	CITIBANK, N.A. (NY)
Upton/Druid Heights	10% to 20% of MFI	low	Synchrony Bank (UT)
Upton/Druid Heights	10% to 20% of MFI	low	Unknown
Upton/Druid Heights	10% to 20% of MFI	low	Capital One Bank (USA), N.A. (VA)
Upton/Druid Heights	10% to 20% of MFI	low	TEXAS CAPITAL BANK (TX)
Upton/Druid Heights	10% to 20% of MFI	low	Synchrony Bank (UT)
Upton/Druid Heights	10% to 20% of MFI	low	American Express, FSB (UT)
Upton/Druid Heights	10% to 20% of MFI	low	WELLS FARGO BANK, N.A. (CA)
Upton/Druid Heights	10% to 20% of MFI	low	Unknown
Upton/Druid Heights	10% to 20% of MFI	low	US BANK NA (WI)
Upton/Druid Heights	10% to 20% of MFI	low	WELLS FARGO BANK, N.A. (CA)
Upton/Druid Heights	10% to 20% of MFI	low	Unknown
Upton/Druid Heights	10% to 20% of MFI	low	American Express, FSB (UT)
Upton/Druid Heights	10% to 20% of MFI	low	Bank of America, N.A. (CA)
Upton/Druid Heights	10% to 20% of MFI	low	Chase Bank USA, NA (DE)
Upton/Druid Heights	10% to 20% of MFI	low	Synchrony Bank (UT)
Upton/Druid Heights	10% to 20% of MFI	low	Bank of America, N.A. (CA)
Upton/Druid Heights	10% to 20% of MFI	low	Chase Bank USA, NA (DE)
Upton/Druid Heights	10% to 20% of MFI	low	Synchrony Bank (UT)
Upton/Druid Heights	10% to 20% of MFI	low	Barrington Bank and Trust (IL)
Upton/Druid Heights	10% to 20% of MFI	low	CITIBANK, N.A. (NY)
Upton/Druid Heights	10% to 20% of MFI	low	US BANK NA (WI)
Upton/Druid Heights	10% to 20% of MFI	low	Capital One Bank (USA), N.A. (VA)
Upton/Druid Heights	10% to 20% of MFI	low	Capital One Bank (USA), N.A. (VA)
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	SUNTRUST BANKS, INC (GA)
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	WELLS FARGO BANK, N.A. (CA)
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	Chase Bank USA, NA (DE)
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	1ST MARINER BANK (MD)
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	Synchrony Bank (UT)
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	Chase Bank USA, NA (DE)
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	EAGLEBANK (MD)
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	Capital One Bank (USA), N.A. (VA)
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	Northwest Bank (PA)
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	American Express, FSB (UT)
Upton/Druid Heights	20% to 30% of MFI	low	American Express, FSB (UT)
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	SUNTRUST BANKS, INC (GA)
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	Chase Bank USA, NA (DE)
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	Synchrony Bank (UT)
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	Capital One Bank (USA), N.A. (VA)
Upton/Druid Heights	20% to 30% of MFI	low	Capital One Bank (USA), N.A. (VA)
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	Chase Bank USA, NA (DE)
Upton/Druid Heights	20% to 30% of MFI	low	Chase Bank USA, NA (DE)
Upton/Druid Heights	20% to 30% of MFI	low	CITIBANK, N.A. (NY)
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	Capital One, N.A. (VA)
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	Chase Bank USA, NA (DE)
Upton/Druid Heights	20% to 30% of MFI	low	Chase Bank USA, NA (DE)
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	American Express Centurion Bk (UT)
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	Capital One Bank (USA), N.A. (VA)
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	Bank of America, N.A. (CA)
Upton/Druid Heights	20% to 30% of MFI	low	Bank of America, N.A. (CA)
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	Chase Bank USA, NA (DE)
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	Bank of America, N.A. (CA)
Upton/Druid Heights	20% to 30% of MFI	low	WELLS FARGO BANK, N.A. (CA)
Upton/Druid Heights	20% to 30% of MFI	low	PNC BANK N.A. (PA)
Upton/Druid Heights	20% to 30% of MFI	low	CITIBANK, N.A. (NY)
Upton/Druid Heights	20% to 30% of MFI	low	Bank of America, N.A. (CA)
Upton/Druid Heights	20% to 30% of MFI	low	BMO Harris Bank, N.A. (IL)
Upton/Druid Heights	20% to 30% of MFI	low	Chase Bank USA, NA (DE)
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	Capital One Bank (USA), N.A. (VA)
Upton/Druid Heights	20% to 30% of MFI	low	MetaBank (IA)
Upton/Druid Heights	20% to 30% of MFI	low	American Express, FSB (UT)
Upton/Druid Heights	20% to 30% of MFI	low	WELLS FARGO BANK, N.A. (CA)
Upton/Druid Heights	20% to 30% of MFI	low	WELLS FARGO BANK, N.A. (CA)
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	Capital One Bank (USA), N.A. (VA)
Upton/Druid Heights	20% to 30% of MFI	low	Chase Bank USA, NA (DE)
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	SUNTRUST BANKS, INC (GA)
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	20% to 30% of MFI	low	Unknown
Upton/Druid Heights	30% to 40% of MFI	low	Unknown
Upton/Druid Heights	30% to 40% of MFI	low	Unknown
Upton/Druid Heights	30% to 40% of MFI	low	Unknown
Upton/Druid Heights	30% to 40% of MFI	low	Unknown
Upton/Druid Heights	30% to 40% of MFI	low	Unknown
Upton/Druid Heights	30% to 40% of MFI	low	Unknown
Upton/Druid Heights	30% to 40% of MFI	low	Unknown
Upton/Druid Heights	30% to 40% of MFI	low	Unknown
Upton/Druid Heights	30% to 40% of MFI	low	Unknown
Upton/Druid Heights	30% to 40% of MFI	low	WELLS FARGO BANK, N.A. (CA)
Upton/Druid Heights	30% to 40% of MFI	low	Chase Bank USA, NA (DE)
Upton/Druid Heights	30% to 40% of MFI	low	Unknown
Upton/Druid Heights	30% to 40% of MFI	low	Unknown
Upton/Druid Heights	30% to 40% of MFI	low	Unknown
Upton/Druid Heights	30% to 40% of MFI	low	Unknown
Upton/Druid Heights	30% to 40% of MFI	low	Unknown
Upton/Druid Heights	30% to 40% of MFI	low	Unknown
Upton/Druid Heights	30% to 40% of MFI	low	Unknown
Upton/Druid Heights	30% to 40% of MFI	low	Chase Bank USA, NA (DE)
Upton/Druid Heights	30% to 40% of MFI	low	Chase Bank USA, NA (DE)
Upton/Druid Heights	30% to 40% of MFI	low	Unknown
Upton/Druid Heights	30% to 40% of MFI	low	Unknown
Upton/Druid Heights	30% to 40% of MFI	low	Unknown
Upton/Druid Heights	30% to 40% of MFI	low	Unknown
Upton/Druid Heights	30% to 40% of MFI	low	Capital One Bank (USA), N.A. (VA)
Upton/Druid Heights	30% to 40% of MFI	low	Chase Bank USA, NA (DE)
Upton/Druid Heights	30% to 40% of MFI	low	Unknown
Upton/Druid Heights	30% to 40% of MFI	low	Unknown
Upton/Druid Heights	30% to 40% of MFI	low	Unknown
Upton/Druid Heights	30% to 40% of MFI	low	Chase Bank USA, NA (DE)
Upton/Druid Heights	30% to 40% of MFI	low	Unknown
Upton/Druid Heights	30% to 40% of MFI	low	Unknown
Upton/Druid Heights	30% to 40% of MFI	low	Unknown
Upton/Druid Heights	30% to 40% of MFI	low	Unknown
Upton/Druid Heights	30% to 40% of MFI	low	Unknown
Upton/Druid Heights	30% to 40% of MFI	low	Unknown
Upton/Druid Heights	40% to 50% of MFI	low	American Express, FSB (UT)
Upton/Druid Heights	40% to 50% of MFI	low	Sandy Spring Bank (MD)
Upton/Druid Heights	40% to 50% of MFI	low	Unknown
Upton/Druid Heights	40% to 50% of MFI	low	Capital One Bank (USA), N.A. (VA)
Upton/Druid Heights	40% to 50% of MFI	low	Synchrony Bank (UT)
Upton/Druid Heights	40% to 50% of MFI	low	American Express, FSB (UT)
Upton/Druid Heights	40% to 50% of MFI	low	Unknown
Upton/Druid Heights	40% to 50% of MFI	low	M&T BANK (NY)
Upton/Druid Heights	40% to 50% of MFI	low	SUNTRUST BANKS, INC (GA)
Upton/Druid Heights	40% to 50% of MFI	low	WELLS FARGO BANK, N.A. (CA)
Upton/Druid Heights	40% to 50% of MFI	low	Unknown
Upton/Druid Heights	40% to 50% of MFI	low	Capital One Bank (USA), N.A. (VA)
Upton/Druid Heights	40% to 50% of MFI	low	Unknown
Upton/Druid Heights	40% to 50% of MFI	low	WELLS FARGO BANK, N.A. (CA)
Upton/Druid Heights	40% to 50% of MFI	low	CITIBANK, N.A. (NY)
Upton/Druid Heights	40% to 50% of MFI	low	American Express, FSB (UT)
Upton/Druid Heights	40% to 50% of MFI	low	Synchrony Bank (UT)
Upton/Druid Heights	40% to 50% of MFI	low	Capital One Bank (USA), N.A. (VA)
Upton/Druid Heights	40% to 50% of MFI	low	Unknown
Upton/Druid Heights	40% to 50% of MFI	low	TEXAS CAPITAL BANK (TX)
Upton/Druid Heights	40% to 50% of MFI	low	Bank of America, N.A. (CA)
Upton/Druid Heights	40% to 50% of MFI	low	Unknown
Upton/Druid Heights	40% to 50% of MFI	low	WELLS FARGO BANK, N.A. (CA)
Upton/Druid Heights	40% to 50% of MFI	low	Synchrony Bank (UT)
Upton/Druid Heights	40% to 50% of MFI	low	WELLS FARGO BANK, N.A. (CA)
Upton/Druid Heights	40% to 50% of MFI	low	Capital One, N.A. (VA)
Upton/Druid Heights	40% to 50% of MFI	low	Capital One Bank (USA), N.A. (VA)
Upton/Druid Heights	40% to 50% of MFI	low	FIRST NATIONAL BANK OF OMAHA (NE)
Upton/Druid Heights	40% to 50% of MFI	low	Synchrony Bank (UT)
Upton/Druid Heights	40% to 50% of MFI	low	FIRST NATIONAL BANK OF OMAHA (NE)
Upton/Druid Heights	40% to 50% of MFI	low	M&T BANK (NY)
Upton/Druid Heights	40% to 50% of MFI	low	SUNTRUST BANKS, INC (GA)
Upton/Druid Heights	40% to 50% of MFI	low	American Express, FSB (UT)
Upton/Druid Heights	40% to 50% of MFI	low	SUNTRUST BANKS, INC (GA)
Upton/Druid Heights	40% to 50% of MFI	low	WELLS FARGO BANK, N.A. (CA)
Upton/Druid Heights	40% to 50% of MFI	low	Synchrony Bank (UT)
Upton/Druid Heights	40% to 50% of MFI	low	PNC BANK N.A. (PA)
Upton/Druid Heights	40% to 50% of MFI	low	M&T BANK (NY)
Upton/Druid Heights	40% to 50% of MFI	low	TEXAS CAPITAL BANK (TX)
Upton/Druid Heights	40% to 50% of MFI	low	Unknown
Upton/Druid Heights	40% to 50% of MFI	low	WELLS FARGO BANK, N.A. (CA)
Upton/Druid Heights	40% to 50% of MFI	low	1ST MARINER BANK (MD)
Upton/Druid Heights	40% to 50% of MFI	low	Unknown
Upton/Druid Heights	40% to 50% of MFI	low	Unknown
Upton/Druid Heights	40% to 50% of MFI	low	Unknown
Upton/Druid Heights	40% to 50% of MFI	low	M&T BANK (NY)
Upton/Druid Heights	40% to 50% of MFI	low	SUNTRUST BANKS, INC (GA)
Upton/Druid Heights	40% to 50% of MFI	low	Unknown
Upton/Druid Heights	40% to 50% of MFI	low	Unknown
Upton/Druid Heights	40% to 50% of MFI	low	Unknown
Upton/Druid Heights	40% to 50% of MFI	low	WELLS FARGO BANK, N.A. (CA)
Upton/Druid Heights	40% to 50% of MFI	low	Unknown
Upton/Druid Heights	40% to 50% of MFI	low	Unknown
Upton/Druid Heights	40% to 50% of MFI	low	Unknown
Upton/Druid Heights	40% to 50% of MFI	low	Unknown
Upton/Druid Heights	40% to 50% of MFI	low	Unknown
Upton/Druid Heights	40% to 50% of MFI	low	Unknown
Upton/Druid Heights	40% to 50% of MFI	low	Unknown
Upton/Druid Heights	40% to 50% of MFI	low	Unknown
Upton/Druid Heights	40% to 50% of MFI	low	Unknown
Upton/Druid Heights	40% to 50% of MFI	low	Unknown
Upton/Druid Heights	40% to 50% of MFI	low	Unknown
Upton/Druid Heights	40% to 50% of MFI	low	Unknown
Upton/Druid Heights	40% to 50% of MFI	low	1ST MARINER BANK (MD)
Upton/Druid Heights	40% to 50% of MFI	low	WELLS FARGO BANK, N.A. (CA)
Upton/Druid Heights	40% to 50% of MFI	low	PNC BANK N.A. (PA)
Upton/Druid Heights	40% to 50% of MFI	low	CITIBANK, N.A. (NY)
Upton/Druid Heights	40% to 50% of MFI	low	Bank of America, N.A. (CA)
Upton/Druid Heights	40% to 50% of MFI	low	Barrington Bank and Trust (IL)
Upton/Druid Heights	40% to 50% of MFI	low	Capital One Bank (USA), N.A. (VA)
Upton/Druid Heights	40% to 50% of MFI	low	MetaBank (IA)
Upton/Druid Heights	40% to 50% of MFI	low	Synchrony Bank (UT)
Upton/Druid Heights	40% to 50% of MFI	low	Unknown
Upton/Druid Heights	40% to 50% of MFI	low	American Express, FSB (UT)
Upton/Druid Heights	40% to 50% of MFI	low	Lake Forest Bank and Trust (IL)
Upton/Druid Heights	40% to 50% of MFI	low	AMERIS BANK (GA)
Upton/Druid Heights	40% to 50% of MFI	low	WELLS FARGO BANK, N.A. (CA)
Upton/Druid Heights	40% to 50% of MFI	low	Unknown
Upton/Druid Heights	40% to 50% of MFI	low	Unknown
Upton/Druid Heights	40% to 50% of MFI	low	Unknown
Upton/Druid Heights	40% to 50% of MFI	low	Unknown
Upton/Druid Heights	40% to 50% of MFI	low	Unknown
Upton/Druid Heights	40% to 50% of MFI	low	SUNTRUST BANKS, INC (GA)
Upton/Druid Heights	40% to 50% of MFI	low	Unknown
Upton/Druid Heights	40% to 50% of MFI	low	Unknown
Upton/Druid Heights	40% to 50% of MFI	low	Unknown
Upton/Druid Heights	40% to 50% of MFI	low	Unknown
Upton/Druid Heights	40% to 50% of MFI	low	Unknown
Upton/Druid Heights	40% to 50% of MFI	low	Capital One Bank (USA), N.A. (VA)
Upton/Druid Heights	40% to 50% of MFI	low	SUNTRUST BANKS, INC (GA)
Upton/Druid Heights	40% to 50% of MFI	low	Northwest Bank (PA)
Upton/Druid Heights	40% to 50% of MFI	low	Unknown
Upton/Druid Heights	40% to 50% of MFI	low	Unknown
Upton/Druid Heights	40% to 50% of MFI	low	Unknown
Upton/Druid Heights	40% to 50% of MFI	low	Unknown
Upton/Druid Heights	40% to 50% of MFI	low	WELLS FARGO BANK, N.A. (CA)
Upton/Druid Heights	40% to 50% of MFI	low	Unknown
Upton/Druid Heights	40% to 50% of MFI	low	Chase Bank USA, NA (DE)
Upton/Druid Heights	40% to 50% of MFI	low	Capital One Bank (USA), N.A. (VA)
Upton/Druid Heights	40% to 50% of MFI	low	Unknown
Upton/Druid Heights	40% to 50% of MFI	low	Unknown
Upton/Druid Heights	40% to 50% of MFI	low	WELLS FARGO BANK, N.A. (CA)
Upton/Druid Heights	40% to 50% of MFI	low	Unknown
Upton/Druid Heights	40% to 50% of MFI	low	Unknown
Upton/Druid Heights	40% to 50% of MFI	low	Unknown
Upton/Druid Heights	40% to 50% of MFI	low	Unknown
Upton/Druid Heights	40% to 50% of MFI	low	Unknown
Upton/Druid Heights	40% to 50% of MFI	low	Chase Bank USA, NA (DE)
Upton/Druid Heights	40% to 50% of MFI	low	Unknown
Upton/Druid Heights	40% to 50% of MFI	low	Unknown
Upton/Druid Heights	40% to 50% of MFI	low	Unknown
Upton/Druid Heights	40% to 50% of MFI	low	Unknown
Upton/Druid Heights	40% to 50% of MFI	low	Chase Bank USA, NA (DE)
Upton/Druid Heights	40% to 50% of MFI	low	Unknown
Upton/Druid Heights	40% to 50% of MFI	low	Sandy Spring Bank (MD)
Upton/Druid Heights	40% to 50% of MFI	low	Unknown
Upton/Druid Heights	40% to 50% of MFI	low	Northwest Bank (PA)
Upton/Druid Heights	40% to 50% of MFI	low	Unknown
Upton/Druid Heights	40% to 50% of MFI	low	SUNTRUST BANKS, INC (GA)
Upton/Druid Heights	40% to 50% of MFI	low	PNC BANK N.A. (PA)
Upton/Druid Heights	40% to 50% of MFI	low	Unknown
Upton/Druid Heights	40% to 50% of MFI	low	Unknown
Upton/Druid Heights	< 10% of Median Family Income (MFI)	low	Capital One Bank (USA), N.A. (VA)
Upton/Druid Heights	< 10% of Median Family Income (MFI)	low	Chase Bank USA, NA (DE)
Upton/Druid Heights	< 10% of Median Family Income (MFI)	low	Capital One Bank (USA), N.A. (VA)
Upton/Druid Heights	< 10% of Median Family Income (MFI)	low	US BANK NA (WI)
Upton/Druid Heights	< 10% of Median Family Income (MFI)	low	American Express, FSB (UT)
Upton/Druid Heights	< 10% of Median Family Income (MFI)	low	Synchrony Bank (UT)
Upton/Druid Heights	< 10% of Median Family Income (MFI)	low	Synchrony Bank (UT)
Upton/Druid Heights	< 10% of Median Family Income (MFI)	low	American Express, FSB (UT)
Upton/Druid Heights	< 10% of Median Family Income (MFI)	low	Unknown
Upton/Druid Heights	< 10% of Median Family Income (MFI)	low	WELLS FARGO BANK, N.A. (CA)
Upton/Druid Heights	< 10% of Median Family Income (MFI)	low	Capital One Bank (USA), N.A. (VA)
Upton/Druid Heights	< 10% of Median Family Income (MFI)	low	US BANK NA (WI)
Upton/Druid Heights	< 10% of Median Family Income (MFI)	low	American Express, FSB (UT)
Upton/Druid Heights	< 10% of Median Family Income (MFI)	low	WELLS FARGO BANK, N.A. (CA)
Upton/Druid Heights	< 10% of Median Family Income (MFI)	low	CITIBANK, N.A. (NY)
Upton/Druid Heights	< 10% of Median Family Income (MFI)	low	CITIBANK, N.A. (NY)
Upton/Druid Heights	< 10% of Median Family Income (MFI)	low	Chase Bank USA, NA (DE)
Upton/Druid Heights	< 10% of Median Family Income (MFI)	low	Synchrony Bank (UT)
Upton/Druid Heights	< 10% of Median Family Income (MFI)	low	Synchrony Bank (UT)